Defining National Spatial Data Infrastructures as

GSDI-9 Conference Proceedings, 6-10 November 2006, Santiago, Chile

Defining National Spatial Data Infrastructures as Complex Adaptive Systems Lukasz Grus, Joep Crompvoets and Arnold Bregt Wageningen University and Research Centre The Netherlands, +31 317474452 [email protected], [email protected], and [email protected] Abstract After two decades of intensive worldwide development of (National) Spatial Data Infrastructure (NSDI) initiatives it is increasingly important to assess the outcomes in order to justify the resources spent on those programs. Many researchers throughout the world have been struggling with building NSDI assessment programs. However it is very difficult to assess NSDIs due to its complex, dynamic and constantly evolving nature. The reason for this difficulty might be that our knowledge of the real forces and mechanisms behind NSDI is still limited. This paper describes a new approach of defining NSDI. It is argued that NSDIs can be treated as Complex Adaptive Systems (Waldrop, 1993; Holland, 1996). Complex Adaptive Systems (CAS) are frequently described by the following characteristics: openness, components, non-linearity, emergency, feedback loops, adaptability, self-organization, multi-understanding, dynamics, unpredictability, sensitivity to initial conditions, scale-independence (fractal building). Both CAS and NSDI characteristics are presented, examined and compared using three case studies. Australian Dutch and Polish SDIs are analyzed in the context of Complex Adaptive Systems. This explorative and comparative study confirms that NSDI has similar properties and patterns of behaviour as CAS. Analyzing NSDI as CAS enriches our understanding of NSDIs’ complex character. As a result it appears possible to better identify and understand the key factors and conditions for NSDI functioning. The presented research is part of the Dutch program Space for Geo-Information “Development of Framework to Assess National Spatial Data Infrastructures”.

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1. Introduction Many countries all over the world have invested large sums of money and effort debating optimal National Spatial Data Infrastructures (NSDI). Until 2002 over 100 countries had initiated projects related to NSDI (Crompvoets, 2006). This means that (from a worldwide perspective) billions of Euros are spent yearly on National SDI development. Existing NSDI developments and implementations differ considerably between countries due to NSDIs complex character but also cultural, economic, historical, societal internal and external factors. Very often the initiatives do not meet the expectations despite the extensive investments and much effort. Up to now these investments have never been audited or evaluated in a systematic and coherent way. It is very difficult to assess NSDIs likely due to its complex, dynamic and constantly evolving nature. Many researchers throughout the world have been struggling with assessing National Spatial Data Infrastructures (Crompvoets et al., 2004; Steudler et al., 2004; Rodriguez-Pabon et al., 2005; Delegado et al., 2005; Kok and van Loenen, 2004; Onsrud, 1998; Masser, 1999; SADL, 2005). Despite all of the aforementioned attempts there is still no coherent and operational methodology and framework to assess National Spatial Data Infrastructure. The reason for this might be that our knowledge of the real forces and mechanisms behind NSDI is still limited. Researchers have been trying to apply various theories in order to describe and better understand the complex nature of NSDIs. The Diffusion of Innovation theory (Rogers, 1995) was used by many researchers (Chan, 2001) like Onsrud and Pinto (1991), Masser (1993), Masser and Onsrud, (1993), Campbell (1996), Masser and Campbell (1996), Chan (1998) in order to better illustrate and understand the development and adoption of GIS and NSDI initiatives within society. Rajabifard et al. (2000) used the Hierarchical Spatial Reasoning (Car, 1997) in order to better describe the complex hierarchical structure of NSDI. De Man (2006) applies the concept of institutionalization in order to explain the linkage of SDIs and spatial data communities and enable the effectiveness and sustainability of SDI. In order to assess NSDI initiatives Steudler et al. (2004) uses the analogy between SDI and Land Administration principles in order to derive the key variables and indicators for evaluating SDI. Kok and van Loenen (2005) apply organizational theory in order to assess the success of NSDI. Eelderink (2006) selected variables that are key for assessing NSDI initiatives in developing countries. The participants of the workshop “Exploring SDI” (Grus et al., 2006) stressed that complex and dynamic nature of NSDI and the variety of its definitions as the main obstacles in assessing NSDIs . All the aforementioned studies confirm that we still need a theory to grasp the principles and complex nature of NSDI. According to Rajabifard (2002) there is still a need for descriptions to actually represent the discrepancies between the role and deliverables of a SDI, and thus, contribute to a simpler, but dynamic, understanding of the complexity of the SDI concept. Chan (2001) claims that perceptions and descriptions of SDI fail to convey SDI’s dynamics and complexity.

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2. Objective The objective of this research is to check whether or not CAS theory can define NSDI. To achieve this, the following sub-objectives were formulated: - preliminary analyzing NSDI as CAS; - translating CAS theory into NSDI using case studies; - defining NSDI as CAS The next three paragraphs (Paragraph 3, 4, 5) present the current knowledge on the concept of NSDI, tackle the complex nature of NSDI and present the principles of Complex Adaptive Systems theory. Paragraph 6 summarizes the methodology leading to translation of CAS theory into NSDI and to formulating a new NSDI definition as CAS. Paragraph 7 presents the results of application of CAS theory into the selected NSDI case studies (Australia, The Netherlands and Poland) and a new definition of NSDI. In paragraph 8 conclusions and recommendations for further research are drawn.

3. National Spatial Data Infrastructures Over last few years, (National) Spatial Data Infrastructure emerged to be an important issue of Geoinformation Science. The general objective of (N)SDI is to provide an environment for accessing and sharing spatial data in order to reduce the data duplication by both users and producers. Based on the literature study and experts opinions (Grus et al., 2006) National Spatial Data Infrastructure can be defined as a dynamic network facility comprising people, data, policy, standards and technology for improved utilization of spatial data and services within a jurisdiction of one country. This research focuses on National SDI (NSDI) because they are measurable, identifiable and sustainable. Additionally they have strong impact on the other levels of the SDI hierarchy (Global, Regional, State and Local) in terms of e.g. core datasets and policy. In this research it is often referred to SDI which is understood in the same way as NSDI except the jurisdictional limitation. Since the Executive Order (Clinton, 1994) in 1994 many countries throughout the world have taken steps to establish NSDIs (Masser, 2005). Until 2002 more then 100 countries were working on NSDI like initiatives (Crompvoets, 2006). The development of NSDI’s clearinghouses – spatial data access facility – is also in progress. According to Crompvoets (2006) the number of National SDI clearinghouses increased rapidly from the first initiative in 1994 in USA to 83 national clearinghouses in April 2005. The increase of clearinghouses reflects the expansion of NSDI initiatives around the world. Large sums of money have been invested into NSDI initiatives over last few years. For example, around €120 million is spent yearly only for clearinghouse management (Crompvoets, 2006). SDI is about the facilitation and coordination of the exchange and sharing of spatial data between stakeholders in the spatial data community (Crompvoets et al., 2004). Spatial data and its sharing go far beyond the potential development of the Geo-Information industry itself. It has the potential to impact widely on society due to its ability to represent a host of important characteristics spatially and thus provide support in diverse areas (Rajabifard et al., 2003). According to Rajabifard et al. (2003) benefits realised through (National) SDI developments generally include:

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Reducing the costs of data production and eliminating duplication of effort; Developing applications more quickly and easily by using existing data and data development standards; Providing better data for decision-making; Saving development effort by using fundamental and standardised data, guidelines and tools; Ability to perform cross-jurisdictional and cross-sectoral decision-making, analysis and operations based on common data and understanding of issues; Expanding market potential through recognition and credibility as an SDI participant as well as through the formation of beneficial partnerships; Providing consolidated directions to vendors regarding required technical features; Facilitation the development of knowledge infrastructure and communication networks.

The list above shows that an operational (National) SDI can improve and make national economy more efficient, enhance decision-making, boost cooperation between different levels of activities, and improve knowledge transfer. A well-operating NSDI could contribute to better cooperation in crucial areas like: sustainable development, national economic development and cooperation, national mapping, national security, environmental monitoring, resource management, urban and regional planning, agricultural and forestry management and maritime relationships (Rajabifard et al., 2003). Two stages of (N)SDI evolution are commonly mentioned in the literature: first and second generation of SDI. First generation SDI can be characterized by implementation of product-based model of SDI (Rajabifard et.al., 2002). Characteristic for this model is linking and integrating databases, data exchange and project-oriented initiatives. The key driver of the first generation was mostly database creation (Masser, 2005). Transition to second generation came around year 2000. According to Rajabifard et.al., (2003) many remarkable events for SDI took place in this time conferences, forums, new projects, publications of new strategies. He mentioned rapid increase in number of countries involved in SDI development and constitution of SDI community where members can share and exchange experiences. Worldwide transition to second generation of SDI was also fostered by development of new SDI strategies in Australia and USA which can be considered as the key SDI players on worldwide scale. According to Masser (2005) the key driving force for second generation development was desire to reuse data collected (to that time) by a wide range of agencies. Crompvoets et al. (2004) mentions the introduction of web services as a main technological indicator of second generation SDI as it is able to fulfil the needs of users and improve the use of spatial data. Second generation of SDI can be characterized by implementation of process-based model (Rajabifard et al., 2002). In this approach SDIs aim is to create framework to facilitate management of information assets. The objective is to provide better communication channels (knowledge infrastructure and capacity building) for the user in order to share and use spatial data. The characteristics of process-based model are establishing active linkage between people and data, coordination of the development and long-term planning. Despite the extensive research on NSDIs initiatives and experience with numerous implementations, decision makers all around the world still did not work out operational implementation guidelines. The understanding of NSDI’s mechanisms is also limited. The research on monitoring and assessment tools for NSDI initiatives is also limited. The definition of NSDI is problematic as well. Chan (2001) collected 11 most popular SDI definitions created by different organizations in different parts of the world in different time.

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4. The complexity of National Spatial Data Infrastructure Large number of SDI definitions demonstrates the disagreement existing between stakeholders on one universal description of SDIs nature. The reason for this could be that SDI is too complex. The complexity is built by dynamic and non-linear interactions between entangled SDI components. It means that the components and interactions cannot be described in a simple and uniform way. In different parts of the world SDI has different character and works in a different way. Because of its complexity it is difficult to implement it in diverse environments in the same way and with the same result. However despite various implementations, all SDI initiatives converge at the same idea to improve the accessibility and utilization of spatial data and information. Complexity of NSDI means that it cannot be understood only in terms of the summation of its parts. All NSDI components produce the value that is rather higher then the value of those components put together. In that sense NSDI is understood as a complex rather than just complicated system. Self-organization of NSDI is reflected in the emergence of the internal systems structure as a result of dynamic and intensive exchange of energy and information between its components. Evolving character of NSDI means that the system constantly adapts to external and internal factors that shape its objectives. This means that NSDI is an open system as it constantly has to interact with its environment. To better understand the aforementioned characteristics the authors view NSDI through the lens of Complex Adaptive Systems (CAS). The next chapter presents in detail the concept of CAS. According to Eoyang (1998) recent research in organizational management, behaviour and psychology indicate that human systems behave as complex adaptive systems. Second generation of NSDI is about building knowledge infrastructure, capacity building, communication and coordination. The core of those actions is human factor. Facilitating the role between data and people is central to the concept of SDI (Rajabifard, 2002). Therefore NSDI, especially second and future generation, can be understood as the one where human factor plays one of the crucial roles. De Man (2006) argues that SDI is a socio-technical assembly. Key NSDI issues like awareness, capacity building, collaboration, knowledge transfer and data sharing all have to do with social factors. The variety of actors and intensity of interactions between them make NSDI complex. It is important then to explore and understand the inherent complexity of NSDI treated as human system. Eoyang (1997) distinguishes two paradigms 1) Newtonian and 2) Complex that can be used to analyze phenomena. The choice depends on the characteristics of the phenomena (see Figure 1.)

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Use Newtonian techniques when the problem is

Use complex techniques when the problem is:

Quiet familiar

New and unique

Well defined

Fuzzy or unknown

Closed to outside influence

Open to outside influences

Related to small number of persons you know well

Related to a large number you don’t know well

One you’ve tried to solve before and succeeded

One you’ve tried to solve before and failed

Linear, the inputs and outputs are clearly distinguishable

Nonlinear, the inputs and outputs are not clearly distinguishable

Figure 1 Newtonian versus complex system’s analysis techniques (Eoyang, 1997)

The choice of the paradigm does not have to be mutually exclusive. However, phenomena like NSDI is quiet new (less than two decades of development), not well defined, not fully explored, changing, not solved and the outcomes are not well known. Therefore, the choice of the complex techniques (CAS) to analyze NSDI could be justified. Understanding SDI as CAS uncovers the mechanisms and forces that shape NSDI initiatives. The next chapter explains the concept of Complex Adaptive Systems.

5. Complex Adaptive Systems Complex Adaptive Systems (CAS) have its roots in studies on chaotic systems (Gleick, 1989; Lorenz, 1993) Kiefer (2006) briefly summarizes the development of CAS research. Studies of various systems in different disciplines led researchers to focus on systems that moved from stable, predictable patterns into unstable, unpredictable behaviour. As work continued, these systems fell into two groups – 1) unpredictable and 2) those that moved through unpredictable states into new, more complex patterns of behaviour. The latter group has attracted attention from a wider group of researchers as Complex Adaptive Systems (Holland, 1996; Waldrop, 1992; Cilliers, 1998; Eoyang, 1996; 1998). The pioneer studies on the concept of Complex Adaptive Systems have been conducted intensively at the independent Interdisciplinary Santa Fe Institute (SFI) http://www.santafe.edu/. Many other scientific groups became interested in the concept of

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CAS and its applications. This resulted in the creation of many research groups at various scientific institutes all over the world devoted to complexity related research1. Many definitions of complex (adaptive) systems exist in a literature. Chiva-Gomez (2003) defines complex adaptive systems as systems made up of heterogeneous agents which interrelate with each other and with their surroundings, and are unlimited in their capabilities to adapt their behaviour as a result of their experience. Waldrop (1992) describes CAS as a dynamic network of many agents (which may represent cells, species, individuals, firms, nations) acting in parallel, constantly acting and reacting to what the other agents are doing. The control of a CAS tends to be highly dispersed and decentralized. If there is to be any coherent behaviour in the system, it has to arise from competition and cooperation among the agents themselves. The overall behaviour of the system is the result of a huge number of decisions made every moment by many individual agents. According to Barnes et al. (2003) CAS are open systems in which different elements interact dynamically to exchange information, self-organize and create many different feedback loops, where relationships between causes and effects are non-linear, and where the systems as a whole has emergent properties that cannot be understood by reference to the component parts. Complex Adaptive Systems have specific characteristics and behavioural patterns that make them distinctive from other types of systems. The following collection of CAS characteristics is based on multiple scientific resources on complex systems (Cilliers, 1998; 2005; Eoyang and Berkas, 1996; 1998; Barnes et al., 2003; Rotmans, 2005; Walldrop, 1992). - Openness – The openness of CAS means that they interact with their environment (Rotmans, 2005). It is also difficult to define clearly where complex and adaptive programmes begin and end (Barnes et al., 2003). CAS are also open to external influences (Eoyang and Berkas, 1998); - Components – The CAS components are many relatively stable and simple building blocks (Cilliers, 2005), that are linked via a mutual interactions (Rotmans, 2005; Eoyang and Berkas, 1998). Holland (1995) states that building blocks are pervasive feature of complex adaptive systems. - Nonlinearity – The nonlinear behaviour of complex adaptive systems means that in CAS it is impossible to determine the value of a second variable, even when a first variable is known (Eoyang, 1996). This means that the changes are the results of external or internal factors boosting or slowing down the system. Cillers (2005) explains nonlinearity by summarizing interactions in CAS as input-output relations that are dynamic. This means that the strength of the interaction changes over time. - Complexity – The complexity of CAS means that the phenomenon that arises because of a great many of the simple components interacting simultaneously (Walldrop, 1992). Complexity also means that the whole of the system is different from the sum of its parts (Eoyang, 2004) which means that complex systems cannot be analyzed only by its parts separately.

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Bandung Fe Institute Official Web (http://www.bandungfe.net/)

Santa Fe Institute (SFI) (http://www.santafe.edu) University of Michigan Center for the Study of Complex Systems (CSCS) (http://www.cscs.umich.edu/) Northwestern Institute on Complex Systems (NICO) (http://www.northwestern.edu/nico/) Max-Planck Institute for Physics of Complex Systems (http://www.mpipks-dresden.mpg.de/) Center for Complex Systems Research (http://www.ccsr.uiuc.edu/) New England Complex Systems Institute (NECSI) (http://www.necsi.org/) Center for Complex Systems (OBUZ) - ISS Warsaw University (http://www.iss.uw.edu.pl/osrodki/obuz/OBUZNEW_ENG/obuz.htm)

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- Emergency – CAS emergency means that its behaviour results from the interactions between components and not from characteristics inherent in the components themselves (Cilliers, 2005). Rotmans (2005) defines emergence as a ‘spontaneous’ development of patterns from the inside of the system. - Feedback loops – Existence of feedback loops mean that the system has tendency to use its own output to make adjustments in its inputs and processes (Eoyang, 1996). Two types of feedback loops can adjust the behaviour of complex adaptive system: negative and positive. Evaluation process is an example of feedback loop, either positive or negative. It is positive when the system learns from the evaluation and enhances its performance, or negative when negative evaluation results discourage program participants. - Adaptability – The adaptability of complex systems mean that they are able to adjust and adapt itself to changes in its environment (Cilliers, 2005; Rotmans, 2005). Adaptation is a result of systems memory another characteristic of CAS. Holland (1995) describes adaptation as system’s change in structure (strategy) based on system experience. Adoption leads to the diversity of patterns. - Self-organization – Self-organization of CAS means that they are able to develop a new system structure. It is a result of the system’s internal constitution and not a result of external management (Rotmans, 2005). According to Eoyang (1996) system self-organizes if it is pushed far enough from equilibrium. Examples of self-organization in human systems are spontaneous group activity, dissenting factions or cliques. Cilliers (1998 – Complexity and Postmodernism) defines self-organization as a process where a system can develop a complex structure from fairly unstructured beginnings. The process happens due to influence both the external environment and the history (memory) of the system. - Multi-understanding – Multi-understanding means that the complex systems may have many different descriptions (Cilliers, 1998). They cannot be reduced to simple definition. - Dynamics – The dynamics of CAS mean that there is constant exchange of information, needs, strategies between the components and actors of a system. - Unpredictability – The unpredictability of CAS is a result of lack of the uniform theory that could describe complex adaptive systems. Because of this, predicting system’s behaviour can be very problematic. To make sense of the output of the complex system we must take into account the mechanisms by which it is produced (Cilliers, 1998). However the prediction will never be with certainty. - Sensitivity to initial conditions – In CAS a very small initial action may have a tremendous future effect. For example, one person’s invention can be a new profitable product line (Eoyang, 1996), or change of one legal document may have tremendous effect on whole society or many organizations. - Scale-independence – CAS are self-similar on different scales (fractal building). This metaphor can be found in organizations where the same features can be seen from the bottom to the top of management chain (Eoyang, 1998) Complex systems are usually accompanied with the same categories of issues (Rotmans, 2005): • The problems that are identified have been there for a long period, usually for decades; • Many parties involved in the coordination of these sectors/systems but the individual parties’ scope for managing these systems is relatively limited; • The relationship for those involved is well established for the most part, and there is hardly any room for manoeuvre; • Parties generally take part in lengthy negotiations about short term incremental renewals or improvements of the existing order; • There is no coherent vision on the long-term future of the specific system;

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For a long time, problems have been addressed by the same actors, following the same outdated rules of the game, within the same old-fashioned institutions; The complexity and corresponding uncertainty are not adequately recognized and seen through by the parties involved; The supplier’s interests weigh more than those of end-users; The end-user has no real freedom of choice and say; Economic interests and values take precedence over societal interests and values.

Those categories of issues are usually complex, uncertain, difficult to manage and hard to grasp (Rotmans, 2005). Those issues do not characterize complex adaptive systems, however they are usually present in those systems. It is therefore interesting to analyze complex systems in the context of identification of those issues and how they cope with them. It can give some indication of the system’s performance. Complex systems research is currently applied in wide range of disciplines like biology, economics, social sciences, policy. It allows for better understanding of the mechanisms and rules of the complex systems.

6. Methodology In order to determine whether or not NSDI can be described as CAS three NSDI case studies: Australian, Dutch and Polish, were carefully analyzed in the context of CAS. The criteria for selection of those case studies was the different development stage of each NSDI, difference in CSI index (Crompvoets, 2006) and availability of information on each of the case study NSDIs. In each of the NSDI cases the key CAS characteristics: Openness, Components, Nonlinearity, Complexity, Emergency, Feedback loops, Adaptability, Self-organization, Multi-understanding, Dynamics , Unpredictability, Sensitivity to initial conditions , Scale-independence were identified and explored. Additionally, the existence of CAS categories of issues (see paragraph 5) were also identified and explored. The presence of most of those characteristics and categories of problems in NSDI case studies would determine whether or not they can be defined as complex adaptive systems. Based on the preliminary analysis of NSDI as CAS and case study NSDIs analysis, it would be attempted to formulate a new definition of NSDI as Complex Adaptive System.

7. Results 7.1 Translating CAS theory into NSDI using case studies The CAS theory is being translated into NSDI by means of three case studies; Australian, Dutch and Polish. They are analyzed in the context of complex adaptive systems’ main characteristics (in italics) and categories of problems. The CAS characteristics are identified and described in those NSDI initiatives. The existence of CAS categories of issues and the ability of solving them within NSDI case initiatives are also described. The case study analysis in the context of CAS is summarized and a new definition of NSDI as CAS is formulated.

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7.2 Australian NSDI as CAS The facts about the Australian SDI are based on Clarke et al. (2003), Chan et al. (2005), Warnest et al. (2005), DITR (2004). The Australian SDI (ASDI) dates back to 1986 when Australian Land Information Committee (since 1991 known as Australian and New Zealand Land Information Committee – ANZLIC and from 2004 – ANZLIC-The spatial data council) was formally established. It was a response to the growing need to coordinate the collection and transfer of land related information and to promote their use. ANZLIC’s role is to facilitate access to spatial data and services provided by many organizations dispersed all across the country. ANZLIC facilitates the development of ASDI towards its vision that Australia’s spatially referenced data, products and services are available and accessible to all users. ANZLIC defines ASDI as comprising people, policies and technologies necessary to enable the use of spatially referenced data through all levels of government, the private and non-profit sectors and academia. ASDI comprises numerous actors playing within national boundaries on different levels. Conceptual model of ASDI framework sets the distributed nature of the ASDI where multiple state/territory agencies provide SDI elements and on national level a Commonwealth SDI integrates them through common technical standards. The distributed nature of ASDI is further replicated in the jurisdictions. Several bodies have emerged in the last years, strengthening the public, private, professional and research SDI organizational infrastructure. On national level the key ASDI players are ANZLIC, Public Sector Mapping Agencies, Spatial Information Industry Action Agenda, Australian Spatial Information Business Association (ASIBA), Spatial Sciences Coalition, Cooperative Research Centres, Intergovernmental Committee on Surveying and Mapping (ICSM). The framework operates through consensus which means that Commonwealth did not legislate or did not force in anyway other players within ASDI. Australian SDI system can be regarded as an open one. According to Clarke (2003) the dynamic nature of SDIs means that the concept of ‘complete’ is inappropriate. This also applies to ASDI where dataset scoreboard, directory audit and pricing policy projects revealed that those fundamental elements are still incomplete and thus open to changes. ANZLIC’s active membership in regional SDI initiative Permanent Committee on GIS Infrastructure for Asia and the Pacific reflects its openness for the cooperation with higher levels of SDI hierarchy. ASDI development requires consultation and cooperation of players to achieve commitment. The high number of various players implies that ASDI’s array of bodies is very heterogeneous and thus must be open to each other to be able to cooperate. The heterogeneity of players in a system and its openness to external factors provides an opportunity of information and energy flow and thus the system self-organize. ANZLIC arose from the need of coordination of land information. The fact that ANZLIC achieved as much in creating ASDI, despite working only through consensus (the Commonwealth do not legislate or force other players to conform to the national policy or standards) and its emergent creation as a response to the needs of GI-sector, gives an evidence about ASDI’s capability to self-organize. ANZLIC initially focused mainly on land administration (cadastral) systems. However, later on it recognized that the SDI concept was much broader. Therefore, ANZLIC adopted changing the term “land-related information’ to “spatial information” and ANZLIC’s broader role became more emphasised. The fact that the assessment of core dataset was suspended in 2002 due to the new ASDI’s work plan gives the evidence of systems adaptability to external factors (work plan change) and non-linear (and thus unpredictable) development (suspension of the assessment process). The components – the characteristic of CAS - are also present in ASDI as it is defined as comprising people, policy and technology. They can be regarded as key ASDI’s building blocks. Emergence of new bodies from the inside of “spatial community” in the last years that strengthened the public, private, 10

professional and research SDI organizational infrastructure can be seen as another characteristic typical for CAS. Those new bodies emerged from within a system that recognized the need of their existence. Replication of distributed NSDI model in the jurisdiction gives the evidence of fractal characteristic existing in ASDI. In other words, similar organizational model is present on all levels. The complexity of ASDI arises from the requirement of a cooperation of 9 governments. Many ASDI players interact and cooperate in decentralized scenario constituting the whole ASDI. The complexity also means that it is not the summation of individual players properties what makes ASDI, but complex and constant interactions between them. The need of CRC-SI identified as critical the implementation of Spatial Information Action Agenda. This can be regarded as positive feedback loop. Its need for existence was identified within ASDI in order to boost its own development by supporting and enhancing national research priorities related to spatial sciences. ANZLIC’s decision to audit Australian Spatial Data Directory (ASDD) in order to check the quality of metadata can be regarded as a positive feedback loop. The assessment resulted in a number of recommendations in order to improve the quality of ASDD. Documents like “Review of the Implementation of the Spatial Information Industry Action Agenda and the Spatial Data Access and Pricing Policy” by Department of Industry, Tourism and Resources of Australian Government (2004) serve as a feedback loop. It gives the critical judgement of the progress in spatial market related actions and enables its improvement. ANZLIC itself, having as the main objective the development of ASDI, provided a self evaluation procedure (feedback loop) for five modules of ASDI (Blake, 2005). The series of ASDI workshops in 2003 can be regarded as another kind of feedback on ASDI development. The dynamic nature of ASDI is reflected in constant flow of information and cooperation between state/territory SDIs and Commonwealth in order to create the environment to enable the use of spatial data. Also the changes, like reviewing the ASDI model by ANZLIC and commencing new definition and vision gives and evidence of its changing and dynamic nature. Multi-understanding is not an issue in Australian SDI. On national level there is already only one ANZLIC’s definition of ASDI. Several problems typical for CAS (please see Chapter. 3) and present in ASDI can disturb its development. For example, long lasting problems like the funding issue is the key limitation on ANZLIC’s ability to develop ASDI (Clarke et al. 2003). ANZLIC’s individual members can only fund their own components and they usually do it having in regard their own needs first. Another problem that cannot be solved immediately is the lack of awareness about the existence of ASDD by the target audience. Other long lasting problems for ASDI are those related to its organization. ANZLIC cannot legally mandate tools for binding national policies. This also falls into the next category of CAS problem that the parties involved has limited managing scope. However, some steps have been taken to solve those kinds of problems. For example, recent developments particularly the establishment of ANZLIC national office, incorporation of PSMA and formations of ASIBA have greatly strengthened the institutional framework. In the way of creating NSDI’s organizational structure its complexity is recognized and better managed. Another problem that is usually present in complex systems is that the supplier’s interests weigh more than those of endusers. ANZLIC solves it by stressing in its definition and vision the balance between the producer (supply side) and user (demand side) sectors of spatial data community. End-users needs are also expressed in pricing policy which seeks to maximise the benefits to the community.

7.3 Dutch NSDI as CAS The facts about the Dutch SDI are based on SADL (2005), Ravi(2003) and Van Loenen and Kok (2002), and Van Loenen (2006).

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The development of Dutch NSDI dates back to 1990 when RAVI – network organization for geoinformation (GI) – was established. Initially, RAVI was an official advisory committee on land information of the Ministry of Spatial Planning and Environment (VROM). In 1993 it changed its status to independent consultative body for geo-information comprising representatives of various public and private sectors. Currently RAVI (GeoNovum from October 2006) is a public platform organization stimulating GI and NSDI development in The Netherlands. RAVI’s mission with respect to the Dutch NSDI is to organize and promote the provision of geo-information required for the performance of public tasks at minimum cost for society as a whole. VROM recognizes itself as the formal geo-coordinator, however NSDI initiative has always been left to selfregulation by the GI-sector (de facto left with RAVI which has no formal powers to compel public agencies to participate in Dutch NSDI). Dutch NSDI development can be described as a set of many bottom-up initiatives. RAVI defines National Geographic Information Infrastructure (NGII) as a collection of policy, data sets, standards, technology (hardware, software and electronic communication) and knowledge providing the user with the geographic information needed to carry out a task (Masser, 1998) In 1992 RAVI presented a Structure plan for land information that soon turned out to be a vision of Dutch NSDI. The vision’s idea was that between the most important core registrations exchange of core data had to be stimulated based on agreement between the responsible authorities of core registrations. The current status of Dutch NSDI convinces that the initial vision is almost complete. There is a need now to set up a new goals and aims for the Dutch NSDI development. In 1995 RAVI initiated building Dutch NSDI clearinghouse (NCGI) and in 1996 it was launched on internet. From 2000-2004 it is being managed and exploited by private sector (Geodan) taking care of its development. By the end of 2002 the structure scheme of GI (NSDI-model) was almost complete and consisted of the following collection of standardized and integrated fundamental datasets: Register of Persons, Register of Companies, Register of Buildings, Base map 1:10 000, Register of standardised addresses, Cadastral Registers, Large scale base map. Despite this core framework, huge amount of organizations produce a large number of geo-datasets without any guidelines and coordination. In 2002, the Dutch Council of Ministries agreed to invest 20 million Euro available for the research and development program ‘Space for Geo-information’. It is aimed at the enhancement of the current geo-information infrastructure situation as well as the necessary innovation for the future. The list of partners of the consortium related to GI sector amounts to approximately 150 (Ravi, 2003). The high number of the Dutch GI participants and close interaction between them make the GIscene very complex. RAVI is untangling this complexity by creating the tangible structure of Dutch NSDI. The components of NSDI are recognized in RAVI’s NSDI definition. RAVI’s activity has no legal and formal basis. According to Masser (2005), Dutch SDI fall into the category of those NSDIs that grown out of existing geographic information coordination activities. The Dutch NSDI initiative was dependent on voluntary rather then mandatory participation. Therefore, the success of the NGII in the Netherlands lies in the self-organization ability if the GI community and creation of the vision in the ‘Structure Plan for Land Information’ in 1992. The openness of the Dutch NSDI is expressed in the RAVI’s cooperation with wide range of parties. RAVI found supporters of the vision outside the geo-information sector, especially in the Ministry of Interior. Dutch SDI is also open for regional SDI initiatives like the European INSPIRE. However the analysis of weaknesses of the geo-information provisions in the Netherlands stresses the introvert character of the GI-sector. The mission of the Dutch knowledge project ‘Space for Geo-Information’ is to create within the following 10 years geo-information network that would be more dynamic and open. This means the network has to be flexibly integrated with adjacent disciplines, exchange knowledge and cooperate with them being a part of wider network. Dutch NSDI’s openness to its environment and its relationships among organizational parts makes it well prepared to anticipate and respond (adapt) to (un)expected 12

influences in its environment. The adaptability of the Dutch NSDI community was evident in 1993. The government discontinued almost all advisory councils. RAVI almost immediately transformed itself into a consultative body for the geo-information sector in that way adapting to the new circumstances. The feedback loop can be seen in the implementation process of NSDI’s vision. When the process of implementing the NSDI vision became a success, the support for the vision started to grow enabling faster realization of the vision postulates. It resulted in completion of the first vision within 10 years. Another type of the positive feedback loop is the research and development program ‘Space for Geo-information’. After the experience with successful implementation of the first NSDI vision, NSDI community recognized the need for further research, development and new vision. This resulted in the investment of 20 million Euros in order to improve and further develop the GI sector in the following years. Creation of ‘Space for Geo-information’ program gives and evidence of the emergence of NSDI sector. It is able to create within itself new behavioural patterns as a result of collaboration between stakeholders. Sensitivity to initial status is also visible in Dutch NGII. RAVI, now independent body, initially was an advisory body of the Ministry VROM. However, the governmental bodies still support the GI initiatives. The GI sector (and RAVI activity) is still recognized as important by governmental bodies. The reason for this support might be that RAVI is still regarded as close to the government due to its historical connection with the ministry (advisory body of VROM). Unpredictability of NGII is evident in recent reorganizations in the structures of the GI sector. This results in the uncertainty on GI and NSDI market. Dutch knowledge project ‘Space for geoinformation’ states that there is no purpose to look at GI development beyond 10 years due to its short history. Moreover, the development of GI sector is strongly dependent on the developments in ICT. The non-linearity of The Dutch SDI development is partly dependent on the external factors like already mentioned ICT or political changes. In that way the development of NSDI can be non-linear depending on the government’s new priorities and strategies that change every 4 years with the elections. However, 10 years of GI-sector activities resulted in broad political recognition of the importance of geo-information. This makes the future development of NSDI promising. There is one Dutch SDI definition provided by RAVI. Therefore the multiunderstanding NSDI concept is not the case in Dutch GI-sector. Despite the successful development of Dutch NSDI with respect to the vision, there are still problems typical for NSDI as CAS. Despite the recognition of the importance of GI provisions among the ministries and more than one decade of NSDI development, there is still limited awareness of the concept of NGII. Therefore the complexity of Dutch SDI might be not well recognized. Another long lasting problem is the limited data access. Dutch clearinghouse NCGI is very supply oriented and is becoming less operational. ‘Space for Geo-information’ program is aiming in changing this by setting a new research on the development of NSDI data delivery service. Another long lasting problem that cannot be solved is that the geo-information provision set up is sectoral and has no coherent concept. There are still problems with data duplication, integration, standardisation and accessibility. Dutch NSDI faces the problem of too many parties involved in its coordination. It is not clear what the managing scope of the NGII participants is. The lead for GI development is not clear. The GII is in an urgent need regarding the leadership issue. Another weakness of the geo-information provisions in the Netherlands is that the sector is introvert and the exchange of knowledge does not function well. This might be related to the CAS category of problem that the relationships between stakeholders are too tight and there is no room for manoeuvre. However, this problem is partially being diminished by RAVI’s openness and active cooperation with wide arena of parties. One of the main threats for geo-information sector is the old coordination-oriented approach that is no longer applicable and there is a need for powerful steering. This is related to CAS category of problem that for the long time problems were addressed by the same actors, followed with the same outdated rules. The fact that Dutch NSDI has no leadership and the coordination is not clear might be the reason that the complexity 13

is not well recognized which is one of the common problems for CAS. In such a complex system, there is a need for a leadership. The dissemination of geo-data is very supply oriented and many organizations are extremely reticent about making data available (see also the long-lasting problem of the lack of clearinghouse). This problem relates to the CAS one that the suppliers interest weigh more than those of end-users. However, user demands are increasingly important to the producers, but the needs of the users are not commonly understood or heard.

7.4 Polish NSDI as CAS The facts about the Polish SDI are based on SADL(2005c), IGiK (2001). Polish NSDI has been emerging for last few years, however its status and structure is still very unclear. First SDI-like initiatives already started in 1970s when National Land Information System was first implemented. Later in 80-ies the system changed and adapted to the conditions of the market and economy (Gazdzicki and Linsenbarth, 2004). Due to many organizational, administrational and political changes in last two decades, those initiatives stopped and the current Polish NSDI initiative is emerging again. The coordinating role of NSDI in Poland was entrusted by the Geodetic and Cartographic Law to the Surveyor General of Poland, the director of the Head Office of Geodesy and Cartography (GUGiK). The other bodies that are the part of Polish SDI are mainly the organs of geodetic and cartographic service (Association of Polish Surveyors, Association of Polish Cartographers, The Institute of Geodesy and Cartography, Polish Spatial Information Association and National Association of GI Systems Users GISPOL). The coordination activities are funded by the Ministry of Infrastructure. The Decree on “National Land Information System” (NSDI) defines the scope and content of the system and bodies responsible for the establishment and management. NSDI is defined by the Geodetic and Cartographic Law as a database, procedures and techniques in order to collect, update and disseminate spatial data. Two components of Polish SDI can be distinguished: core components (reference datasets) managed by the Surveyor General and thematic components managed by various ministries. The current status of Polish NSDI can be characterized as a patchwork of more that 100 spatial information systems existing across the country at different levels. One of the objectives of NSDI should be the integration of those initiatives however the degree of coordination is not clear. Between 1998 and 2000 a research project entitled “The concept of the Polish Spatial Information System” was commissioned by the Ministry of the Interior and Administration. The objective of this project was to propose a general concept of NSDI in Poland. Up to now the implementation of the postulates of this research is very limited and marginal. The dynamic and much entangled relationships between many key NSDI players and institutions manifest the complexity of Polish NSDI. The task to coordinate 100 spatial information system dispersed across the country is already complex. When we look back and analyze the very dynamic and promising NSDI initiatives in 70s (the concept of Information System TEREN), 80s (Multipurpose Cadastre initiative) and current initiatives, it is striking how non-linear and unpredictable the development of NSDI can be. Most of the initiatives were suspended or not successful due to external factors like political system change or administration reform. However, the emergent property of Polish GI community results in creation within the system new bodies that constitute NSDI initiative. Semi-formal mandate to coordinate the operation of the Polish NSDI by GUGiK may give some evidence of the self-organization ability of the system. 14

However, the attempts to formalize the NSDI creation and its structure by strong geodetic lobby leave no space for other players and thus the self-organization is rather limited. The main Polish NSDI players are connected within well established and influential geodesy community. Even the legal acts commission the coordination of the Polish NSDI to the geodesy bodies. Their experience and expertise with spatial data cannot be questioned. However, for example GISPOL association of GI users (stemming from geodesy members) as its objectives see (among others): opposing to the dissemination of geodetic data; giving preference of the supporters of its actions. The first postulate raise questions about the openness of geodesy bodies and thus NSDI’s to the environment. The second postulate de facto may reject critics that might provide a kind of positive feedback loop to the system. If NSDI is treated as CAS, openness is crucial for the system to be able to cooperate with other parties, anticipate changes in the environment and be able to adapt to changes in its environment. Limited openness might be the one of the impediments of the successful NSDI development. The difficulty with adaptation is visible in still limited compliance of the Polish NSDI to the INSPIRE initiative (SADL, 2005c). The governmental decree giving the coordinating role of Polish NSDI to the Surveyor General (which is the highest geodesy body) resulted in total domination of the present Polish NSDI scene by geodetic bodies. Sensitivity to initial conditions – the characteristic of CAS - is visible in Polish NSDI. The result of Multi-understanding of the concept of Polish NSDI is that the answer to the question about the existence of NSDI in Poland can be either positive or negative depending on the interpretation of the concept of NSDI (Gazdzicki, 2004). This fact gives more confusion to already disorganized Polish NSDI scene. However, the building blocks (components) of NSDI can be identified in Geodetic and Cartographic Law NSDI definition as a database, procedures and techniques. The research project “The concept of the Polish Spatial Information System” might be an example of the feedback loop. After a period of attempts to create NSDI and based on lessons learned, the Ministry of Interior and Administration ordered a research project in order to formulate the comprehensive concept of Polish NSDI. However, due to formal and organizational constraints (no formal mechanism to implement the postulates from the concept) the implementation is very limited. The main long-lasting problem on the Polish NSDI scene is that the concept, objectives and implementation plan for the initiative is not clearly defined. Polish NSDI scene has no coherent framework strategy or vision of the development (Iwaniak and Sliwinski, 2006). This might be the main impediment of the development of NSDI initiative. The general behavioural pattern of Polish NSDI players seems to have no aim defined. There is lack of coherent vision on the long term future of NSDI. This results in the difficulty with setting up an operational NSDI. Another CAS like problem is that the relationships between stakeholders are tight. The majority of Polish NSDI players stem from geodesy community. They have similar point of view on the issues connected with NSDI and thus the relationships between them are very tight. Such closed configuration doesn’t leave much space for the flexibility and manoeuvring. Another category of CAS problem is that the suppliers’ interests weigh more than those of end-users. GUGiK is the main data supplier for the Polish NSDI. However it is very reluctant to make their data accessible to the users. Iwaniak and Sliwinski (2006) argue that making geodetic data available on internet would be the important milestone for the development of Polish NSDI. Presently, no operational NSDI clearinghouse exists. Another CAS like problem is that for long time the same problems were addressed by the same actors following the same outdated rules within the same institutions. Gazdzicki (2005) lists obsolete law on geodesy, cartography and cadastre, lack of common vision, administrative barriers and excessive influence of politicians as the main disadvantages of present GI situation in Poland. Those problems have been discussed for long time, everybody agree that they have to be solved but they still exist.

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7.5 Case studies summary and discussion The aim of analyzing three NSDI case studies was to investigate to what extent CAS theory can describe NSDI. Most of the CAS key characteristics could be identified in NSDI initiatives. The investigated National SDIs must also deal with similar to CAS categories of issues. This means that CAS theory might describe NSDI and help to better understand the mechanisms and key aspects of its functioning from the complexity perspective. The investigation to what extent CAS theory fits NSDI description was based on the identification of CAS characteristics and categories of issues in three NSDI case initiatives. Not all of the CAS characteristics and categories of issues could be clearly identified in case study countries. This might be due to the limited information available on each of the NSDI initiative. This might be also due to the fact that not all CAS characteristics are present in NSDI initiatives. The presented summary contains only those characteristics and categories of CAS issues that could be identified in case studies. However, the study confirms that NSDI’s behaviour resembles complex adaptive system. Self-organization ability of all of the analyzed NSDI could be identified in all of the cases. This property seems to be an important variable for NSDI further development. Rajabifard (2006) stresses the importance of sub-national level role and greater private sector involvement in the future SDI generation. SDI environment enabling better self-organization within NSDI community might help to increase the involvement of the private sector and enable self initiatives moving the NSDI to the third generation stage. The Australian SDI components, despite fairly chaotic and not legalized structure, self-organized creating operational ASDI with visible players, structure and clear task division. The basis for Dutch NGII success is perceived to lie especially in the self-organization of GI-community as a sector (SADL, 2005b). The shortcoming of Polish NSDI might be limited self-organizational ability of the GI-community due to formalized and top-down approach of NSDI functioning. The Openness of NSDI initiative reflects its preparation for cooperation and involvement of large and diverse number of players. According to Rajabifard (2006) the next SDI’s generation bottomup vision stresses the importance of diversity and heterogeneity of SDI players. The system to be heterogeneous must be open to let the components interact freely despite their diversity. The level of openness of GI community to diverse SDI participants is then important characteristic of SDI readiness for future generation. Australian SDI’s wide array of bodies and their smooth cooperation is the evidence of ASDI being an open system. Interaction between Dutch GI sector and the broader political arena (listed as one of the basis of Dutch NGII success – (SADL, 2005b)) gives an evidence of Dutch GI sector openness to other GI players. One of the most important impediments of SDI development might be their homogenous and thus closed to other player’s character. Adaptability of NSDI means that the system is able to transform itself and adjust to inevitable changes in its environment. It also means that the system is not afraid of innovations, takes risk of changes in order to be up to date and to understand current users’ needs. This characteristic is visible in Australian and Dutch SDI. The adaptation of Polish SDI is visible (INSPIRE) however it s also very slow and limited due to mainly organizational constraints. Another two properties of CAS which are visible in all of the case studies is non-linearity and unpredictability of their development. Non-linear behaviour is the result of very complex and 16

unpredictable internal and external environment of NSDI. The non-linear behaviour of Polish NSDI was caused mainly by changing political environment. Several NSDI initiatives have been started since 70’s but none of them developed gradually into operational NSDI. Australian and Dutch NSDI development is less non-linear and unpredictable then the Polish one due to the governmental support and recognition of the importance of NSDI initiatives in those two countries. This gave the NSDI continuous support and reduced the unpredictability of system’s state. This resulted in gradual and constant development of NSDI which nowadays can be regarded as operational. The existence of feedback mechanisms allows the system to improve itself by revising its actions. Various evaluation programmes, revisions and new strategies for NSDI development serve as a regular update of NSDI performance. They assure that the actions taken by GI community are toward the National goal of SDI. In the analyzed case studies such feedback mechanisms are identified in Australia (Spatial Information Action Agenda, self-evaluation procedures) and Netherlands (Space for Geo-Information). Components of NSDI create the basic system’s structure and they were identified in all cases. In all of the analyzed NSDI cases basic NSDI components like data, technology, standards, people and access network are visible building blocks of NSDI structure. Fractal building was identified only in Australian case due to SDI’s clear jurisdictional structure. National ASDI is divided into jurisdictional SDIs that share similar structure, objectives and mechanisms as superior ASDI. In two other cases the lower NSDI levels are less visible due to the non federated country’s structure. However it can be assumed that on regional and local levels SDI’s structure is replicated having data, technology, policy, access network and people as the main structural framework. Dynamic nature of analyzed NSDIs is visible in constant flow of information between components, evolution of the system’s structure and change of objectives and user needs that continuously shape National SDIs. Emergence of various NSDI bodies and programmes as a result of collaboration and interaction of NSDI stakeholders is visible in all of the cases. In Australia, the emergence of various public, private, professional and research bodies strengthened SDI’s organizational structure. In The Netherlands the emergence of the Space for Geo-information programme was the result of longlasting cooperation of many bodies within NSDI environment. The emergent property of Polish NSDI is visible in various SDI-related initiatives that regularly have been taking place since 70s. NSDI’s Sensitivity to its initial status could be identified in The Netherlands and Poland. Ravi’s initial status as a part of the Ministry VROM resulted in the future recognition and support from the governmental bodies for RAVI’s actions (and thus creation of NSDI) despite its present independent character. In Poland initial legal delegation of NSDI tasks to the main geodesy bodies resulted in their present domination of NSDI-related activities. Multi-understanding of NSDI concept is visible in Poland. Depending on the interpretation of NSDI’s definition we can say about the existence or lack of NSDI in Poland. In Australian and Dutch case the multi-understanding of NSDI concept is limited due to relatively long history, broad recognition of the concept of NSDI and agreed uniform definition of NSDI. The analysis of NSDI case studies confirms the existence of common CAS categories of issues mentioned by Rotmans (2005) (see Chapter 4.). Polish NSDI initiative have to deal with long17

lasting problems that concept, objectives and implementation plan for the initiative haven’t been defined despite the relatively long period of attempts to set up NSDI . For The Netherlands the problems that lasted for the longest period relate to limited data access, too many parties involved in coordination and that the geo-information provision is too sectoral. In Australia the funding issue, organizational aspect of ANZLIC are the main problematic issues that have been lasting for a long time. Lack of coherent vision in Complex Adaptive Systems creates chaotic behaviour of its agents without certain aim. One of the impediments of Polish SDI might be lack of strategy or vision that all NSDI partners would agree on. On the contrary, Australian and Dutch SDI’s complex behaviour is ordered by the existence of the concept (vision) of National SDI. Therefore the actions of agents operating in such complex system as NSDI converge to common target defined in the vision. The stress on suppliers’ interest weighting more than the user’s is present in all three cases. However countries like Australia try to solve it stressing in NSDI definition and vision the balance between producer and user side. One of the weaknesses of the Dutch NSDI is that the data dissemination is very supply oriented. However the user’s demands are increasingly important to the producers. For Poland, the supplier’s interest is still in front of the interest of the user’s. The main data supplier is dominant on GI-market and is very reluctant to make its data accessible to the users. The close character of Polish NSDI, same key actors with similar point of view on NSDI related issues do not leave much space for manoeuvring. One of the weaknesses of Dutch NSDI is too introvert character of GI-sector. This also doesn’t leave much space for manoeuvring and flexibility. NSDI complexity is recognized and managed in Australian case in the way that NSDI organizational structure in order to reduce the complexity has been created in rather loose way leaving space for changes and involvement of various participants. In Dutch NSDI case the coordination and leadership is not well defined, therefore the NSDI’s complexity might still be not well understood. In Poland, NSDI is too simplified to geodesy oriented initiative and therefore its complexity might be still not recognized. Table 3 summarizes the CAS characteristics and table 4 the CAS issues identified in each of the case study country. Table 1. Summary of CAS characteristics per each case study country.

Openness

Components Non-linearity

Australia Very open; heterogeneity of ASDI members governmental and private. ASDI openness to regional SDI initiatives (PCGIAP) ANZLIC’s definition clearly defines ASDI components. Non-linear development of NSDI is diminished by the strong awareness of the need of ASDI existence.

The Netherlands Poland Rather open; RAVI cooperates Limited openness of with wide range of parties, SDI community. finds supporters of the vision outside NSDI however the GI sector should be less introvert. RAVI’s definition of Dutch SDI recognizes the components Non-linearity is reduced as there is recognition of NSDI’s need of existence and continuous support 18

Three NSDI components could be defined Very non-linear; many SDI initiatives attempts since 70s

Feedback loop

Emergence

Adaptability Multi-understanding Self-organization

Dynamism Unpredictable

Sensitivity to initial conditions Fractal Building

need of ASDI existence. Assessment initiatives of ASDI’s components.

Emergence of new bodies from the inside of GI community in the last years strengthened the public, private, professional and research SDI ANZLIC adapted changing itself from land to spatial oriented Multi-understanding is limited due to the existence the definition ASDI self-organizes despite relatively nonformal structure and leadership

‘Space for Geo-information as emerging initiative as a result of interaction of GI-community

Limited feedback mechanism. “The concept of Polish SDI” as a feedback however its postulates were not executed. Since 2004 emergence of new bodies creating Polish SDI

Adapting its status to changing environment

Slow adaptation to INSPIRE SDI program

Multi-understanding is limited due to the existence the definition Self-organizing of GIcommunity as a source of success in meeting the vision

Constant flow of information between state/territory SDI Unpredictability is quiet low as ASDI has set up structure and strategy.

Very dynamic – postulates of the vision achieved in 10 years

Polish SDI is understood in many different ways. Limited selforganization ability due to top-down and very formalized approach n/a*

n/a*

Early RAVI’s membership of the ministry VROM resulted in long-lasting governmental support for its actions n/a*

National SDI structure replicated in jurisdictional SDIs * n/a – information was not found

‘Space for Geo-information’ program as a positive feedback loop.

Unpredictability is diminished by the existence of the awareness.

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Unpredictability (and non-linear behaviour) results from not stable governmental priorities towards NSDI since 70s Geodetic bodies are dominant since 70s in the creation of Polish SDI n/a*

Table 2. Summary of the main categories of CAS issues identified in case studies.

Complexity issues Main long-lasting issues

Australia Funding issue, organizational aspect is still problematic

Vision

Vision exists

Suppliers vs. user’s interest

Try to build the balance between the user’s and producer’s side. n/a*

Space for manoeuvring

Complexity Rather well recognition recognized * n/a – information was not found

The Netherlands Limited data access, too many parties involved in coordination and the geo-information provision is too sectoral Vision exists Data dissemination is too supply oriented. Too introvert character of GI-sector mentioned as a weakness.

Still problems with recognition

Poland Concept, objectives and implementation plan haven’t been defined yet.

Lack of coherent vision Supplier’s interest is still in front of the interest of the user’s The same key actors with similar point of view on NSDI related issues and their closed character leave no much space for changes Not recognized

7.6 Defining NSDI as CAS Based on the literature review on NSDI and Complex Adaptive Systems, preliminary analysis of NSDI in the context of CAS and analyzing case study NSDI initiatives in order to find CAS characteristics and common issues, it can be assumed that the character and behaviour of National SDI initiatives resemble the Complex Adaptive System. Based on the analogy between NSDI initiatives and CAS a new NSDI definition is proposed: National Spatial Data Infrastructure is a complex adaptive system for facilitating the access and sharing of spatial datasets and services in the jurisdiction of one country needed to support spatially related issues. This definition explains the complex character of NSDI by applying complex adaptive system theory. It is possible to describe NSDI by means of twelve CAS characteristics: openness, components, non-linearity, emergency, feedback loops, adaptability, self-organization, multiunderstanding, dynamics, unpredictability, sensitivity to initial conditions, and scaleindependence (fractal building) in order to grasp and analyze in a systematic way the inherent NSDI complexity.

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8. Conclusions and recommendations for further research The aim of this research was to investigate if the Complex Adaptive System theory can describe National Spatial Data Infrastructure in order to better explore and understand its complex and dynamic character. The experiment to search for CAS characteristics and categories of problems in the Australian, Dutch and Polish National Spatial Data Infrastructure confirms that National Spatial Data Infrastructure in those countries resembles Complex Adaptive System. Most of CAS characteristics could be identified in the case studies, and the main issues, they have to cope with, resemble those typical for CAS. As the three analyzed NSDIs represent three different types and development NSDI development stages, they are representative for other NSDI initiatives. It can be assumed then that the behaviour of other National SDI initiatives will also resemble the Complex Adaptive System. Based on that reasoning it was possible to define NSDI as Complex Adaptive System. The through definition and understanding of NSDI functioning is the first step to build its assessment framework. Exploring NSDI as CAS reveals its key assessment aspects. Based on the analysis of three NSDI case studies in the context of CAS theory it was possible to identify twelve NSDI characteristics that are crucial for its functioning as a complex system. Those are components, non-linearity, emergency, feedback loops, adaptability, self-organization, multiunderstanding, dynamics, unpredictability, sensitivity to initial conditions, scale-independence (fractal building). Some of the selected CAS characteristics and issues could be used in future studies on NSDI assessment as they describe its key structural and behavioural areas from the complexity perspective. The choice of the set of assessment variables would be determined by the purpose of the assessment. NSDI case study analysis revealed also the existence of main issues that CAS and NSDI have to cope with. The issues like the presence of long-lasting problem, lack of coherent vision, suppliers versus end users’ needs, space for manoeuvring and complexity recognition are present in NSDI initiatives and have to be taken into account while assessing NSDI. It is then recommended for further research to identify the indicators that would measure each of the complexity characteristics present in NSDI and include them in the assessment framework. Because of the fact that NSDIs are complex adaptive systems, it means that they cannot be understood only by the summation of its parts. The value produced by its components is greater then their sum. This fact has serious implications for NSDI’s assessment. The overall outcomes of NSDI performance should be measured rather then only summarizing the performances of its parts. It is also important to note that NSDI assessment framework cannot be restricted exclusively to the assessment of NSDI only from the one complexity perspective but also should include other NSDI dimensions. For example NSDI’s objective of “…facilitating the access and sharing of spatial datasets and services …” is equally important and has to be assessed. Therefore the further research on NSDI assessment framework should also concentrate on evaluating how the final NSDI objectives and users’ needs towards NSDI are met. NSDIs worldwide are also on the different maturity level and some are developing already beyond the First and Second generation SDI. Therefore, the assessment framework should also measure the dynamic aspect of the development of NSDI initiatives towards the vision (Rajabifard, 2006) of next generation of NSDI. Because of the dynamic nature of CAS its baseline can constantly change. Therefore its 21

assessment framework should allow for redesign and be flexible (Eoyang, 1998). NSDI assessment framework would be then a multi-faceted set of tools taking into account various perceptions of NSDI.

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